Giant Magnetoresistance and Related Properties of Rare Earth Manganates and Other Oxide Systems

Giant magnetoresistance (GMR), which was until recently confined to magnetic layered and granular materials, as well as doped magnetic semiconductors, occurs in manganate perovskites of the general formula Ln(1-x)A(x)MnO(3) (Ln = rare earth; A = divalent ion). These manganates are ferromagnetic at or above a certain value of x (or Mn4+ content) and become metallic at temperatures below the curie temperature, T-c. GMR is generally a maximum close to T-c or the insulator-metal (I-M) transition temperature, T-im. The T-c and %MR are markedly affected by the size of the A site cation, [r(A)], thereby affording a useful electronic phase diagram when T-c or T-im is plotted against [r(A)]. We discuss GMR and related properties of manganates in polycrystalline, thin-film, and single-crystal forms and point out certain commonalities and correlations. We also examine some unusual features in the electron-transport properties of manganates, in particular charge-ordering effects. Charge ordering is crucially dependent on [r(A)] or the e(g) band width, and the charge-ordered insulating state transforms to a metallic ferromagnetic state on the application of a magnetic field.

Thermopower and nature of the hole-doped states in LaMnO3 and related systems showing giant magnetoresistance

We have measured the thermopower (S) of hole-doped LaMnO3 systems in order to see its dependence on the Mn4+ content as well as to investigate other crucial factors that determine S. We have carried out hole doping (creation of Mn4+ by two distinct means, namely, by the substitution of La by divalent cations such as Ca and Sr and by self-doping without aliovalent substitution). The thermopower is sensitive not only to the hole concentration but also to the process employed for hole doping, which we explain as arising from the differences in the nature of the hole-doped states. We also point out a general trend in the dependence of S on hole concentration at high temperatures (T> T-c), similar to that found in the normal-state thermopower of the cuprates.

Structural characteristics of a giant tropical liana and its mode of canopy spread in an alien environment

To circumvent the practical difficulties in research on tropical rainforest lianas in their natural habitat due to prevailing weather conditions, dense camouflaging vegetation and problems in transporting equipment for experimental investigations, Entada pursaetha DC (syn. Entada scandens Benth., Leguminosae) was grown inside a research campus in a dry subtropical environment. A solitary genet has attained a gigantic size in 17 years, infesting crowns of semi-evergreen trees growing in an area roughly equivalent to 1.6 ha. It has used aerially formed, cable-like stolons for navigating and spreading its canopy across tree gaps. Some of its parts which had remained unseen in its natural habitat due to dense vegetation are described. The attained size of this liana in a climatically different environment raises the question as to why it is restricted to evergreen rainforests. Some research problems for which this liana will be useful are pointed out.

Synthesis of nanoparticles of the giant dielectric material, CaCu3Ti4O12 from a precursor route

A complex oxalate precursor, CaCu3(TiO)(4)(C2O4)(8)center dot 9H(2)O, (CCT-OX), was synthesized and the precipitate that obtained was confirmed to be monophasic by the wet chemical analyses, X-ray diffraction, FTIR absorption and TG/DTA analyses. The thermal decomposition of this oxalate precursor led to the formation of phase-pure calcium copper titanate, CaCu3Ti4O12, (CCTO) at a parts per thousand yen680A degrees C. The bright-field TEM micrographs revealed that the size of the as synthesized crystallites to be in the 30-80 nm range. The powders so obtained had excellent sinterability resulting in high density ceramics which exhibited giant dielectric constants upto 40000 (1 kHz) at 25A degrees C, accompanied by low dielectric losses.

Analyzing multiple spike trains with nonparametric granger causality

Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons limultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.

Statistical significance of sequential firing patterns in multi-neuronal spike trains

Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

Inferring neuronal network connectivity from spike data: A temporal data mining approach

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent d evelopments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.

Effect of the addition of B2O3 and BaO-B2O3-SiO2 glasses on the microstructure and dielectric properties of giant dielectric constant material CaCu3Ti4O12

The effect of the addition of glassy phases on the microstructure and dielectric properties of CaCu3Ti4O12 (CCTO) ceramics was investigated. Both single-component (B2O3) and multi-cornponent (30wt% BaO-60wt% B2O3-10wt% SiO2 (BBS)) glass systems were chosen to study their effect on the density, microstructure and dielectric properties of CCTO. Addition of an optimum amount of B2O3 glass facilitated grain growth and an increase in dielectric constant. However, further increase in the B2O3 content resulted in its segregation at the grain boundaries associated with a reduction in the grain size. In contrast, BBS glass addition resulted in well-faceted grains and increase in the dielectric constant and decrease in the dielectric loss. An internal barrier layer capacitance (IBLC) model was invoked to correlate the dielectric constant with the grain size in these samples. (c) 2007 Elsevier Inc. All rights reserved.

Long-lived giant number fluctuations in a swarming granular nematic

Coherently moving flocks of birds, beasts, or bacteria are examples of living matter with spontaneous orientational order. How do these systems differ from thermal equilibrium systems with such liquid crystalline order? Working with a fluidized monolayer of macroscopic rods in the nematic liquid crystalline phase, we find giant number fluctuations consistent with a standard deviation growing linearly with the mean, in contrast to any situation where the central limit theorem applies. These fluctuations are long-lived, decaying only as a logarithmic function of time. This shows that flocking, coherent motion, and large-scale inhomogeneity can appear in a system in which particles do not communicate except by contact.

Giant magnons in the D1-D5 system

We study giant magnons in the the D1-D5 system from both the boundary CFT and as classical solutions of the string sigma model in AdS(3) x S-3 x T-4. Re-examining earlier studies of the symmetric product conformal field theory we argue that giant magnons in the symmetric product are BPS states in a centrally extended SU(1 vertical bar 1) x SU(1 vertical bar 1) superalgebra with two more additional central charges. The magnons carry these additional central charges locally but globally they vanish. Using a spin chain description of these magnons and the extended superalgebra we show that these magnons obey a dispersion relation which is periodic in momentum. We then identify these states on the string theory side and show that here too they are BPS in the same centrally extended algebra and obey the same dispersion relation which is periodic in momentum. This dispersion relation arises as the BPS condition for the extended algebra and is similar to that of magnons in N = 4 Yang-Mills Yang-Mills.

Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains

We consider the problem of detecting statistically significant sequential patterns in multineuronal spike trains. These patterns are characterized by ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data-mining scheme to efficiently discover such patterns, which occur often enough in the data. Here we propose a method to determine the statistical significance of such repeating patterns. The novelty of our approach is that we use a compound null hypothesis that not only includes models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a probabilistic model that captures the counting process and use this to derive a test of significance for rejecting such a compound null hypothesis. The structure of our null hypothesis also allows us to rank-order different significant patterns. We illustrate the effectiveness of our approach using spike trains generated with a simulator.

A Wavelet based Teager Energy Operator for Spike Detection in Microelectrode Array Recordings

Spike detection in neural recordings is the initial step in the creation of brain machine interfaces. The Teager energy operator (TEO) treats a spike as an increase in the `local' energy and detects this increase. The performance of TEO in detecting action potential spikes suffers due to its sensitivity to the frequency of spikes in the presence of noise which is present in microelectrode array (MEA) recordings. The multiresolution TEO (mTEO) method overcomes this shortcoming of the TEO by tuning the parameter k to an optimal value m so as to match to frequency of the spike. In this paper, we present an algorithm for the mTEO using the multiresolution structure of wavelets along with inbuilt lowpass filtering of the subband signals. The algorithm is efficient and can be implemented for real-time processing of neural signals for spike detection. The performance of the algorithm is tested on a simulated neural signal with 10 spike templates obtained from [14]. The background noise is modeled as a colored Gaussian random process. Using the noise standard deviation and autocorrelation functions obtained from recorded data, background noise was simulated by an autoregressive (AR(5)) filter. The simulations show a spike detection accuracy of 90%and above with less than 5% false positives at an SNR of 2.35 dB as compared to 80% accuracy and 10% false positives reported [6] on simulated neural signals.

The Formation, Numbers And Radio Output Of Giant Radio Galaxies

The numbers and mean radio luminosities of giant radio galaxies (GRGs) have been calculated for redshifts up to z = 0.6, assuming a sensitivity limit of 1 Jy at 1 GHz for the observations. The estimates are obtained with a model for the beam propagation, first through the hot gaseaous halo around the parent galaxy, and thereafter, through the even hotter but less dense intergalactic medium. The model is able to accurately reproduce the observed numbers and mean radio luminosities of GRGs at redshifts of less than 0.1, and it predicts that a somewhat larger number of GRGs should be found at redshifts of greater than 0.1.

Giant planar Hall effect in pulsed laser deposited permalloy films

Ni80Fe20 thin films with high orientation were grown on Si(1 0 0) using pulsed laser ablation. The anisotropic magnetoresistance (AMR) and the planar Hall measurements show a 2.5% resistance anisotropy and a 45% planar Hall voltage change for magnetic field sweep of 10 Oe. The planar Hall sensitivity dR/dH was found to be 900 Omega T-1 compared with a previously reported maximum of 340 Omega T-1 in the same system.Also these films are found to withstand repeated thermal cycling up to 110 degrees C and the Hall sensitivity remains constant within this temperature range. This combination of properties makes the system highly suitable for low magnetic field sensors, particularly in geomagnetic and biosensor applications. To elucidate this, we have demonstrated that these sensors are sensitive to Earth's magnetic field. These results are compared with the sputter deposited films which have a very low AMR and planar Hall voltage change as compared with the films grown by PLD. The possible reasons for these contrasting characteristics are also discussed.